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About ontologies Melissa Haendel. And who am I that I am giving you this talk? Melissa Haendel Anatomist, developmental neuroscientist, molecular biologist,

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Presentation on theme: "About ontologies Melissa Haendel. And who am I that I am giving you this talk? Melissa Haendel Anatomist, developmental neuroscientist, molecular biologist,"— Presentation transcript:

1 About ontologies Melissa Haendel

2 And who am I that I am giving you this talk? Melissa Haendel Anatomist, developmental neuroscientist, molecular biologist, curator, ontologist Most ontologists become ontologists because they have a need to classify and compute on a specific domain

3 Information retrieval from text-based resources is hard: OMIM Query# of records “large bone”785 "enlarged bone"156156 "big bones"16 "huge bones"4 "massive bones"28 "hyperplastic bones"12 "hyperplastic bone"40 "bone hyperplasia"134 "increased bone growth"612

4 MouseEcotope GlyProt DiabetInGene GluChem sphingolipid transporter activity Use of the same defined term indicates the same meaning under different annotation circumstances Common vocabularies allow grouping of disparate data

5 http://www.mkbergman.com/?m=20070516 The controlled vocabulary spectrum Bottom line: you get what you pay for. Ontologies are expensive but powerful. OBO

6 Why build an ontology? A simple example Number of genes annotated to each of the following brain parts in an ontology: brain 20 part_of hindbrain 15 part_of rhombomere 10 Query brain without ontology 20 Query brain with ontology 45 Ontologies can facilitate grouping and retrieval of data

7 An ontology is a classification vocabulary Philosophers: Ontology = The study of being as a branch of philosophy Bioinformaticists: Domain ontology = representing a specific knowledge base Features of ontologies: 1.Terms are defined 2.Terms are arranged in a hierarchy 3.Relationships between the terms are defined 4. Expressed in a knowledge representation language Some well known ontologies are: SnoMED, Foundational Model of Anatomy, Gene Ontology, Linnean Taxonomy of species

8 Defining ontology classes, a beginner’s guide An X =Y that has one or more differentiating characteristics. where Y is the is_a parent of X. Definition: Blue cylinder = Cylinder that is blue. Definition: cylinder = Surface formed by the set of lines perpendicular to a plane, which pass through a given circle in that plane. is_a These types are Disjoint: A cylinder can be either blue or red by definition, but not both. Definitions specify necessary and sufficient conditions. is_a Definition: Red cylinder = Cylinder that is red. Y X

9 Subsumption reasoning: The simplest ontology reasoning is_a entity organism cat mammal animal is_a human is_a instance_of PeanutChris Shaffer

10 Hierarchy types Simple taxonomy Directed acyclic graph

11 Subject —— Property —— Object brain (class)has_partneuron (class) PloS 2009 (instance)has_author Melissa (instance) Chris (instance) ownsred tie (instance) Ontology classes, instances, and relations are stored as sentences

12 The True Path Rule cuticle synthesis --[i] chitin metabolism cell wall biosynthesis --[i] chitin metabolism ----[i] chitin biosynthesis ----[i] chitin catabolism chitin metabolism --[i] chitin biosynthesis --[i] chitin catabolism --[i] cuticle chitin metabolism ----[i] cuticle chitin biosynthesis ----[i] cuticle chitin catabolism --[i] cell wall chitin metabolism ----[i] cell wall chitin biosynthesis ----[i] cell wall chitin catabolism GO Before:GO After: BUT: A fly chitin synthase could be annotated to chitin biosynthesis, and appear in a query for genes annotated to cell wall biosynthesis (and its children), which makes no sense because flies don't have cell walls. NOW: all the child terms can be followed up to chitin metabolism, but cuticle chitin metabolism terms do not trace back to cell wall terms, so all the paths are true. The pathway from a child term all the way up to its top level parent(s) must be universally true.

13 Where does the True Path Rule come from? Transitivity. Some relations are transitive, and apply across all levels of the hierarchy. For example, a cat is_a mammal, and a mammal is_a vertebrate SO a cat is_a vertebrate => This is the true path rule and is because the is_a relation is transitive. Some properties are not transitive. For example, head has_quality round. and, head part_of organism. So is the organism round? Of course not! BUT, eyes are part_of head, and head part_of organism, SO eye part_of organism is true, because part_of is a tranistive relation. Relations are logically defined in a common relation ontology or within each ontology that uses them. ≠ >

14 Domain ontologies are organized according to upper ontologies that specify the general types of things that exist RELATION TO TIME GRANULARITY CONTINUANTOCCURRENT INDEPENDENTDEPENDENT ORGAN AND ORGANISM Organism (NCBI Taxonomy) Anatomical Entity (FMA, CARO) Organ Function (FMP, CPRO) Phenotypic Quality (PaTO) Biological Process (GO) CELL AND CELLULAR COMPONENT Cell (CL) Cellular Component (FMA, GO) Cellular Function (GO) MOLECULE Molecule (ChEBI, SO, RnaO, PrO) Molecular Function (GO) Molecular Process (GO) => Classification according to these higher level types helps ensure the True Path Rule holds

15 Universality Often, the order of the terms in an assertion will matter: We can assert adult transformation_of child but not child transformation_of adult More about using ontology classes and relations Univocity Terms should have the same meanings on every occasion of use. (= They should refer to the same universal types) Basic ontological relations such as is_a and part_of should be used in the same way by all ontologies

16 Common ontology languages OWL is a recognized standard, more tools available, less restricted expressivity OBO simple, more easily readable, used by many biologists, restricted expressivity DNA sequencing DNA sequencing OBI Branch derived DNA sequencing is a sequencing process which uses deoxyribonucleic acid as input and results in a the creation of DNA sequence information artifact using a DNA sequencer instrument. Philippe Rocca-Serra Genomic deletions of OFD1 account for 23% of oral-facial-digital type 1 syndrome after negative DNA sequencing. Thauvin-Robinet C, Franco B, Saugier-Veber P, Aral B, Gigot N, Donzel A, Van Maldergem L, Bieth E, Layet V, Mathieu M, Teebi A, Lespinasse J, Callier P, Mugneret F, Masurel-Paulet A, Gautier E, Huet F, Teyssier JR, Tosi M, Frébourg T, Faivre L. Hum Mutat. 2008 Nov 19. PMID: 19023858 immune response assay immune response assay Is an assay with the objective to determine information about an immune response [Term] id: OBI:0000626 name: DNA sequencing def:"DNA sequencing is a sequencing process which uses deoxyribonucleic acid as input and results in a the creation of DNA sequence information artifact using a DNA sequencer instrument." [OBI:sourced "OBI Branch derived"] is_a: OBI:0600047 ! sequencing assay [Term] id: OBI:0000626 name: DNA sequencing def:"DNA sequencing is a sequencing process which uses deoxyribonucleic acid as input and results in a the creation of DNA sequence information artifact using a DNA sequencer instrument." [OBI:sourced "OBI Branch derived"] is_a: OBI:0600047 ! sequencing assay

17 Ontology Lookup Service at EBI http://www.ebi.ac.uk/ontology-lookup/

18 Bioportal at the NCBO http://bioportal.bioontology.org/

19 Ontology Editors (and viewers) http://oboedit.org/ OBOEdit- OBO ontology editor and viewer Protégé - OWL ontology editor and viewer http://protege.stanford.edu/

20 Ontologies can help reconcile annotation inconsistencies

21 Developmental Biology, Scott Gilbert, 6 th ed. Using ontologies for error checking Text match mapping Fruit fly ‘tibia’Human ‘tibia’ UBERON: tibia UBERON: bone is_a Vertebrata Drosophila melanogaster part_of Homo sapiens is_a only_in_taxon part_of NOT is_a

22 Using ontologies for alignment of similar vocabularies

23 Information Content (IC) is based on depth within the ontology and annotation frequency Using ontologies for computation

24 Why do we need all these rules and standards for ontology use and construction? Automatic reasoning to infer related classes Annotation consistency Error checking Alignment with other ontologies Computation Ontologies must be intelligible to both: HumansMachines


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